!pip install folium
import folium
import matplotlib.pyplot as plt 
from sklearn.linear_model import LinearRegression
from sklearn import linear_model
from datetime import datetime 
import requests #get request
import pandas as pd #pandas
import numpy as np #
import sqlite3
import matplotlib.pyplot as plt
import random 
import seaborn as sns

Requirement already satisfied: folium in /opt/conda/lib/python3.8/site-packages (0.11.0)
Requirement already satisfied: jinja2>=2.9 in /opt/conda/lib/python3.8/site-packages (from folium) (2.11.2)
Requirement already satisfied: numpy in /opt/conda/lib/python3.8/site-packages (from folium) (1.19.1)
Requirement already satisfied: requests in /opt/conda/lib/python3.8/site-packages (from folium) (2.24.0)
Requirement already satisfied: branca>=0.3.0 in /opt/conda/lib/python3.8/site-packages (from folium) (0.4.1)
Requirement already satisfied: MarkupSafe>=0.23 in /opt/conda/lib/python3.8/site-packages (from jinja2>=2.9->folium) (1.1.1)
Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.8/site-packages (from requests->folium) (2020.6.20)
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Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/lib/python3.8/site-packages (from requests->folium) (1.25.10)
Requirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.8/site-packages (from requests->folium) (2.10)

seattleCrime = pd.read_csv("SPD_Crime_Data__2008-Present.csv")

seattleCrime

(seattleCrime['Crime Against Category']).unique()

array(['SOCIETY', 'PROPERTY', 'PERSON', 'NOT_A_CRIME'], dtype=object)

(seattleCrime['Offense Parent Group']).unique()

array(['DRUG/NARCOTIC OFFENSES', 'LARCENY-THEFT', 'ROBBERY',
       'DESTRUCTION/DAMAGE/VANDALISM OF PROPERTY',
       'DRIVING UNDER THE INFLUENCE', 'FRAUD OFFENSES',
       'KIDNAPPING/ABDUCTION', 'COUNTERFEITING/FORGERY',
       'PORNOGRAPHY/OBSCENE MATERIAL', 'EXTORTION/BLACKMAIL',
       'MOTOR VEHICLE THEFT', 'SEX OFFENSES', 'STOLEN PROPERTY OFFENSES',
       'EMBEZZLEMENT', 'WEAPON LAW VIOLATIONS', 'ARSON',
       'HUMAN TRAFFICKING', 'LIQUOR LAW VIOLATIONS', 'BAD CHECKS',
       'PROSTITUTION OFFENSES', 'FAMILY OFFENSES, NONVIOLENT',
       'CURFEW/LOITERING/VAGRANCY VIOLATIONS', 'ANIMAL CRUELTY',
       'HOMICIDE OFFENSES', 'PEEPING TOM', 'SEX OFFENSES, CONSENSUAL',
       'DRUNKENNESS', 'BRIBERY', 'GAMBLING OFFENSES',
       'TRESPASS OF REAL PROPERTY', 'ASSAULT OFFENSES',
       'BURGLARY/BREAKING&ENTERING'], dtype=object)

array_1=[]
array_2=[]
array_3=[]
for x in range(0,3000):
    array_1.append(seattleCrime.loc[x]['Offense Parent Group'])
    array_2.append(seattleCrime.loc[x]['Crime Against Category'])
    array_3.append(seattleCrime.loc[x]['Offense ID'])

plt.scatter(array_2,array_1)

<matplotlib.collections.PathCollection at 0x7f26f3da6820>

seattleTidy = pd.read_csv("SPD_Crime_Data__2008-Present.csv")
seattleTidy.dropna(inplace=True)

seattleTidy=seattleTidy.drop("Offense End DateTime",axis=1)
seattleTidy=seattleTidy.drop("Offense Code",axis=1)

crimeCounts = sorted(seattleTidy["Offense Parent Group"].unique())
crimeCounts2=[]
temp_df = seattleTidy.copy()
index = 0

for ele in crimeCounts:
    crimeCounts2.append((ele, temp_df.groupby("Offense Parent Group").count()["Offense"][0:].values[index])) 
    index += 1

crimeCounts2 = sorted(crimeCounts2, key=lambda x: x[1], reverse = True)
crimeCounts2[0:10]

[('LARCENY-THEFT', 220201),
 ('BURGLARY/BREAKING&ENTERING', 62254),
 ('MOTOR VEHICLE THEFT', 45195),
 ('DESTRUCTION/DAMAGE/VANDALISM OF PROPERTY', 42639),
 ('FRAUD OFFENSES', 35439),
 ('ASSAULT OFFENSES', 24189),
 ('TRESPASS OF REAL PROPERTY', 5015),
 ('ROBBERY', 4056),
 ('DRUG/NARCOTIC OFFENSES', 3846),
 ('BAD CHECKS', 2916)]

top_5_crimes = crimeCounts2[0:5]
top_5_crimes

[('LARCENY-THEFT', 220201),
 ('BURGLARY/BREAKING&ENTERING', 62254),
 ('MOTOR VEHICLE THEFT', 45195),
 ('DESTRUCTION/DAMAGE/VANDALISM OF PROPERTY', 42639),
 ('FRAUD OFFENSES', 35439)]

time_format = "%Y-%m-%d %H:%M:%S"

for idx in seattleTidy.index:
    piInString=str(seattleTidy.at[idx,"Offense Start DateTime"])
    seattleTidy.at[idx,"Offense Start DateTime"] = datetime.strptime(piInString,time_format)
    seattleTidy.at[idx,"Report DateTime"] = datetime.strptime(seattleTidy.at[idx,"Report DateTime"],time_format)

seattleTidy.head()

crimes = {}

for i in range(len(top_5_crimes)):
    crimes[top_5_crimes[i][0]] = [] 

for idx in seattleTidy.index: 
    
    t = seattleTidy.at[idx,"Offense Parent Group"]

    if t in crimes: #IF ITS A TOP CRIME TYPE
        hour = seattleTidy.at[idx, "Offense Start DateTime"].hour 
        crimes[t].append(hour)   

for i in crimes:      
    hist, bin_edges = np.histogram(crimes[i], bins=24)
    plt.hist(crimes[i],bins=24,color="red",edgecolor='black', linewidth=1.2)
    plt.ylabel("Frequency of " + i)
    plt.xlabel("Hour of Day")
    plt.title(i + " happenings per Hour")
    plt.show()

seattleTidy['SEASON OF OCCURENCE'] = None


for idx in seattleTidy.index:
    curr_month = seattleTidy.at[idx, "Offense Start DateTime"].month
    
    if curr_month == 12 or (1 <= curr_month and curr_month <= 2):
        seattleTidy.at[idx,'SEASON OF OCCURENCE'] = "Winter"
    elif 3 <= curr_month and curr_month <= 5:
        seattleTidy.at[idx,'SEASON OF OCCURENCE'] = "Spring"
    elif 6 <= curr_month and curr_month <= 8:
        seattleTidy.at[idx,'SEASON OF OCCURENCE'] = "Summer"
    elif 9 <= curr_month and curr_month <= 11:
        seattleTidy.at[idx,'SEASON OF OCCURENCE'] = "Fall"

seattleTidy.head()

seattleTidy['Year Start'] = None


for idx in seattleTidy.index:
    curr_year = seattleTidy.at[idx, "Offense Start DateTime"].year
    
    seattleTidy.at[idx,'Year Start'] = curr_year

seattleTidy.head()

crimes2 = {}

for i in range(len(top_5_crimes)):
    crimes2[top_5_crimes[i][0]] = [] 

for idx in seattleTidy.index: 
    
    t = seattleTidy.at[idx,"Offense Parent Group"]

    if t in crimes2: #IF ITS A TOP CRIME TYPE
        year = seattleTidy.at[idx, "Offense Start DateTime"].year
        crimes2[t].append(year)        

for i in crimes2:      
    hist, bin_edges = np.histogram(crimes2[i], bins=24)

    plt.hist(crimes2[i],bins=24,color="green",edgecolor='black', linewidth=1.2)
    plt.ylabel("Frequency of " + i)
    plt.xlabel("Year")
    plt.title(i + " happenings per Year")
    plt.show()

#season_list = [['Winter'], ['Spring'], ['Summer'], ['Fall']]
 
#NEW HASH FOR TOP_5_CRIMES (JUST NAMES->NOT TUPLES)
#top_5_crimes_histo = []

#for i in top_5_crimes:
  #  top_5_crimes_histo.append(i[0])


#HASH FOR THE TYPES->SEASONS-> HOURS
#crimes_seasons_hours = {}

#for i in top_5_crimes_histo:
 #   crimes_seasons_hours[i] = {"Winter":[], "Spring":[], "Summer":[], "Fall":[]}
    
#FOR EACH SEASON, CHECK IF THE ROWS IN THE DATAFRAME HAVE A CERTAIN TYPE AND ADD THEIR HOURS ACCORDINGLY TO OUR HASH
#for season in season_list:
  #  for index,row in seattleTidy[seattleTidy["SEASON OF OCCURENCE"].isin(season)].iterrows():
     #   t = row['Offense Parent Group']
      #  if t in top_5_crimes_histo:
       #     crimes_seasons_hours[t][season[0]].append(row['Offense Start DateTime'].hour)

#crimes_seasons_hours

seattleTidy

crimeSeason = sorted(seattleTidy["SEASON OF OCCURENCE"].unique())
crimeSeason2=[]
tempSeason_df = seattleTidy.copy()
index = 0

for ele in crimeSeason:
    crimeSeason2.append((ele, tempSeason_df.groupby("SEASON OF OCCURENCE").count()["Offense"][0:].values[index])) 
    index += 1

#crimeSeason2 = sorted(crimeSeason2, key=lambda x: x[1], reverse = True)
crimeSeason2

[('Fall', 116834), ('Spring', 117270), ('Summer', 114119), ('Winter', 106137)]

def swapList(newList): 
    size = len(newList) 
      
    # Swapping  
    temp = newList[0] 
    newList[0] = newList[size - 1] 
    newList[size - 1] = temp 
      
    return newList

lis=crimeSeason2
lis[0], lis[-1] = lis[-1], lis[0]
lis
#re order the list of seasons with number of crimes committed to correct order

[('Winter', 106137), ('Spring', 117270), ('Summer', 114119), ('Fall', 116834)]

crimeSeason2Copy=lis

graphOfSeasons = [(elem1, elem2) for elem1, elem2 in crimeSeason2Copy]
for (a,b) in graphOfSeasons:

    b=b.item()
    print(type(a),type(b))

<class 'str'> <class 'int'>
<class 'str'> <class 'int'>
<class 'str'> <class 'int'>
<class 'str'> <class 'int'>

fig = plt.figure()
ax = fig.add_axes([0,0,1,1])
langs = ['Winter', 'Spring', 'Summer', 'Fall']
students = [106137,117270,114119,116834]
ax.bar(langs,students)
plt.title("Crimes Committed Per Season From 2020 to 2008") 
plt.xlabel("Season")
plt.ylabel("Number of Crimes Committed")
plt.show()

plt.figure(figsize=(20,10))
plt.plot(*zip(*crimeSeason2Copy))
plt.title("Crimes Committed Per Season From 2008 to 2020") 
plt.xlabel("Season")
plt.ylabel("Number of Crimes Committed")
plt.show()

#for cri in crimes_seasons_hours: #FOR EACH CRIME
    #multi_x = []
    #legend_season_list = []
    #weights =[]
    
    #for season in crimes_seasons_hours[cri]: #4 SEASONS FOR EACH CRIME
        #legend_season_list.append(season)
       # multi_x.append(crimes_seasons_hours[cri][season]) #years FOR THE CRIME
       # weights.append(np.ones_like(crimes_seasons_hours[cri][season])/float(len(crimes_seasons_hours[cri][season])))
   # plt.figure(figsize=(16,6))
   # plt.hist(multi_x,10,histtype = "bar",weights=weights, label=legend_season_list) #PLOT THE DATA

    #plt.title(cri + " Occurances per Year") #LABELS
    #plt.xlabel("Years " )
    #plt.ylabel("Percentage of " + cri)
    
   # plt.legend()
   # plt.show()
   
 #('LARCENY-THEFT', 220201),('BURGLARY/BREAKING&ENTERING', 62254) ('MOTOR VEHICLE THEFT', 45195), ('DESTRUCTION/DAMAGE/VANDALISM OF PROPERTY', 42639), ('FRAUD OFFENSES', 35439)]

crime_copy=top_5_crimes

testList2 = [(elem1, elem2) for elem1, elem2 in crime_copy]
for (t,w) in testList2:

    w=w.item()
    print(type(t),type(w))

<class 'str'> <class 'int'>
<class 'str'> <class 'int'>
<class 'str'> <class 'int'>
<class 'str'> <class 'int'>
<class 'str'> <class 'int'>

#plt.figure(figsize=(20,10))
fig2 = plt.figure(figsize=(20,10))
ax2 = fig2.add_axes([0,0,1,1])
TypeParentOffenseGroup = ['LARCENY-THEFT', 'BURGLARY/BREAKING & ENTERING', 'MOTOR VEHICLE THEFT', 'DESTRUCTION/DAMAGE/VANDALISM OF PROPERTY','FRAUD OFFENSES']
numC = [220201,62254,45195,42639,35439]
ax2.bar(TypeParentOffenseGroup,numC)
plt.title("Crimes Committed for Top 5 Offense Parent Groups") 
plt.xlabel("Offense Parent Group")
plt.ylabel("Number of Crimes Committed")
plt.show()

plt.figure(figsize=(20,10))
plt.plot(*zip(*testList2))
plt.title("Crimes Committed in Each Parent Offense Group") 
plt.xlabel("Offense Parent Group")
plt.ylabel("Number of Crimes Committed")
plt.show()

a=seattleTidy['Offense']
a

1         Theft of Motor Vehicle Parts or Accessories
2                                             Robbery
3            Destruction/Damage/Vandalism of Property
7            Destruction/Damage/Vandalism of Property
9            Destruction/Damage/Vandalism of Property
                             ...                     
874055                            Motor Vehicle Theft
874061                   Burglary/Breaking & Entering
874063                            Motor Vehicle Theft
874065                            Motor Vehicle Theft
874068                            Motor Vehicle Theft
Name: Offense, Length: 454360, dtype: object

crimewCounts = sorted(seattleTidy["Offense"].unique())
crimewCounts2=[]
tempw_df = seattleCrime.copy()
index = 0

for ele in crimewCounts:
    crimewCounts2.append((ele, tempw_df.groupby("Offense").count()["Offense Parent Group"][0:].values[index])) 
    index += 1

crimewCounts2 = sorted(crimewCounts2, key=lambda x: x[1], reverse = True)
crimewCounts2[0:10]

[('Burglary/Breaking & Entering', 95733),
 ('Destruction/Damage/Vandalism of Property', 79975),
 ('All Other Larceny', 73614),
 ('Robbery', 52191),
 ('Pornography/Obscene Material', 28505),
 ('Aggravated Assault', 27413),
 ('Drug/Narcotic Violations', 22852),
 ('Wire Fraud', 20223),
 ('Credit Card/Automated Teller Machine Fraud', 17723),
 ('Driving Under the Influence', 15813)]

top5Offenses=crimewCounts2[0:5]

top5Offenses

[('Burglary/Breaking & Entering', 95733),
 ('Destruction/Damage/Vandalism of Property', 79975),
 ('All Other Larceny', 73614),
 ('Robbery', 52191),
 ('Pornography/Obscene Material', 28505)]

heatMapCSV=seattleTidy
heatMapCSV.dropna(inplace=True)
heatMapCSV

heatMapCSV=heatMapCSV.drop("Group A B",axis=1)
heatMapCSV=heatMapCSV.drop("Precinct",axis=1)
heatMapCSV=heatMapCSV.drop("Sector",axis=1)
heatMapCSV=heatMapCSV.drop("Beat",axis=1)
heatMapCSV=heatMapCSV.drop("MCPP",axis=1)
heatMapCSV=heatMapCSV.drop("Report DateTime",axis=1)
heatMapCSV=heatMapCSV.drop("Offense ID",axis=1) 
heatMapCSV=heatMapCSV.drop("100 Block Address",axis=1)
heatMapCSV=heatMapCSV.drop("SEASON OF OCCURENCE",axis=1)
#heatMapCSV=heatMapCSV.drop("Year Start",axis=1)
heatMapCSV=heatMapCSV.drop("Report Number",axis=1)
#heatMapCSV=heatMapCSV.drop("Longitude",axis=1)
#heatMapCSV=heatMapCSV.drop("Latitude",axis=1)
#heatMapCSV=heatMapCSV.drop("Crime Against Category",axis=1)
#heatMapCSV=heatMapCSV.drop("Offense Parent Group",axis=1)
heatMapCSV.reset_index()
heatMapCSV

heatMapCSV['Month of OCCURENCE'] = None


for idx in heatMapCSV.index:
    curr_month = heatMapCSV.at[idx, "Offense Start DateTime"].month
 
    if curr_month == 12:
        heatMapCSV.at[idx,'Month of OCCURENCE'] = "December"
    
    elif curr_month == 11:
        heatMapCSV.at[idx,'Month of OCCURENCE'] = "November"
    
    elif curr_month == 10:
        heatMapCSV.at[idx,'Month of OCCURENCE'] = "October"
    
    elif curr_month == 9:
        heatMapCSV.at[idx,'Month of OCCURENCE'] = "September"
    
    elif curr_month == 8:        
        heatMapCSV.at[idx,'Month of OCCURENCE'] = "August"
    
    elif curr_month == 7:
        heatMapCSV.at[idx,'Month of OCCURENCE'] = "July"
   
    elif curr_month == 6:
        heatMapCSV.at[idx,'Month of OCCURENCE'] = "June"
    elif curr_month == 5:
        heatMapCSV.at[idx,'Month of OCCURENCE'] = "May"
    elif curr_month == 4:
        heatMapCSV.at[idx,'Month of OCCURENCE'] = "April"
    elif curr_month == 3:
        heatMapCSV.at[idx,'Month of OCCURENCE'] = "March"
    elif curr_month == 2:
        heatMapCSV.at[idx,'Month of OCCURENCE'] = "February"
    elif curr_month == 1:
        heatMapCSV.at[idx,'Month of OCCURENCE'] = "January"

heatMapCSV.head()

heatPlot=heatMapCSV
heatPlot=heatPlot.drop("Offense Start DateTime",axis=1)
heatPlot

map_model = folium.Map(location=[47.6062, -122.331], zoom_start=11)
map_model

map_parentGroup=folium.Map(location=[47.6062, -122.331], zoom_start=11)
map_parentGroup

map_crimeAgainst=folium.Map(location=[47.6062, -122.331], zoom_start=11)
map_crimeAgainst

locationsCrime = heatMapCSV[['Latitude', 'Longitude']]
locationsCrime

locationsCrimeList=locationsCrime.values.tolist()
locationsCrimeList

[[47.67511789, -122.323399063],
 [47.66638407, -122.29955218],
 [47.64292734, -122.384864805],
 [47.68052695, -122.355309065],
 [47.69756434, -122.30489081],
 [47.61468385, -122.316845012],
 [47.66247336, -122.32072010600001],
 [47.5859559, -122.315976829],
 [47.61932364, -122.320863694],
 [47.54294056, -122.32290903200001],
 [47.62394954, -122.361938917],
 [47.57140627, -122.33200668399999],
 [47.70233163, -122.33452432],
 [47.61859552, -122.353751129],
 [47.630894399999995, -122.36180477399999],
 [47.61221899, -122.308863594],
 [47.61221899, -122.308863594],
 [47.71354798, -122.34483426799999],
 [47.55638651, -122.314783435],
 [47.57999734, -122.30489459799999],
 [47.66218058, -122.313115709],
 [47.60709807, -122.30893628],
 [47.57876794, -122.329988664],
 [47.60512615, -122.33049384200001],
 [47.65912603, -122.34563939],
 [47.51654951, -122.378095285],
 [47.58323011, -122.383821146],
 [47.60291195, -122.334607817],
 [47.60169935, -122.320159539],
 [47.66007201, -122.34562812],
 [47.67889074, -122.26222809299999],
 [47.66365981, -122.37218016],
 [47.61018167, -122.33511980200001],
 [47.72503555, -122.34499682799999],
 [47.72503555, -122.34499682799999],
 [47.65137209, -122.38755847600001],
 [47.7086028, -122.32461515799999],
 [47.59750785, -122.322052376],
 [47.70400593, -122.316877599],
 [47.61704694, -122.324495896],
 [47.58435803, -122.299022274],
 [47.60960404, -122.32922111],
 [47.54195105, -122.376509787],
 [47.70599117, -122.35545968299999],
 [47.66365981, -122.37218016],
 [47.60047410000001, -122.33287805299999],
 [47.616134100000004, -122.350186371],
 [47.52193839, -122.364542433],
 [47.5165395, -122.28512018],
 [47.63595428, -122.366339921],
 [47.62460516, -122.362586854],
 [47.66329603, -122.368605586],
 [47.61764399, -122.303753445],
 [47.61597109, -122.352887455],
 [47.51258367, -122.26472355899999],
 [47.64217288, -122.398309451],
 [47.5360414, -122.36129766200001],
 [47.61588661, -122.337879034],
 [47.61393023, -122.327804574],
 [47.60218964, -122.316781855],
 [47.62107587, -122.32089167299999],
 [47.59709367, -122.325063697],
 [47.55200342, -122.379318965],
 [47.55200342, -122.379318965],
 [47.62454524, -122.336479326],
 [47.57019913, -122.280730889],
 [47.61090902, -122.33501787600001],
 [47.60241242, -122.331082199],
 [47.57927478, -122.377542525],
 [47.65046915, -122.35448367],
 [47.5992282, -122.310924822],
 [47.60872352, -122.340630935],
 [47.59678343, -122.333268209],
 [47.61236106, -122.317913914],
 [47.64650426, -122.32519843799999],
 [47.65001037, -122.34979117299999],
 [47.7086028, -122.32461515799999],
 [47.66867356, -122.388860707],
 [47.59076389, -122.317280369],
 [47.62758727, -122.344937685],
 [47.5985085, -122.286922048],
 [47.71354798, -122.34483426799999],
 [47.71354798, -122.34483426799999],
 [47.61264032, -122.324320572],
 [47.62500382, -122.351483825],
 [47.60882096, -122.34001342200001],
 [47.65982286, -122.31548087200001],
 [47.56935997, -122.392108224],
 [47.60951744, -122.338348304],
 [47.69906761, -122.341927731],
 [47.68253569, -122.300744931],
 [47.65980204, -122.31374637399999],
 [47.66221425, -122.31840882200001],
 [47.72323367, -122.35554670799999],
 [47.66595739, -122.382666321],
 [47.61097516, -122.338146742],
 [47.61730887, -122.35316864],
 [47.67032171, -122.290822445],
 [47.61730887, -122.35316864],
 [47.56558373, -122.377447628],
 [47.56473744, -122.3874491],
 [47.61669063, -122.346170622],
 [47.56742553, -122.37744548799999],
 [47.61588924, -122.300082009],
 [47.66129873, -122.31763816200001],
 [47.61523114, -122.323415475],
 [47.61628073, -122.33357712700001],
 [47.64254854, -122.35555354],
 [47.54761638, -122.334314673],
 [47.68592294, -122.32697455],
 [47.65912662, -122.34669439700001],
 [47.72323212, -122.354866194],
 [47.64650426, -122.32519843799999],
 [47.57215315, -122.37709168200001],
 [47.63239628, -122.28268729],
 [47.66292138, -122.365097046],
 [47.62586164, -122.363883734],
 [47.65832864, -122.314363316],
 [47.6158847, -122.33294187],
 [47.65604983, -122.35446663],
 [47.592553499999994, -122.306241505],
 [47.54098986, -122.354956677],
 [47.57291861, -122.294005649],
 [47.59748648, -122.31027812299999],
 [47.51343904, -122.252409725],
 [47.66867758, -122.38392862100001],
 [47.61090902, -122.33501787600001],
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 ...]

(heatPlot['Year Start']).unique()

array([2020, 2019, 2016, 2007, 2018, 2015, 2014, 2017, 2013, 2009, 2010,
       2004, 1987, 2011, 2012, 2006, 2008, 1999, 2003, 1929, 1993, 2002,
       2005, 1995, 1991, 2001, 2000, 1998, 1997, 1994, 1996, 1980, 1992,
       1915, 1953, 1989], dtype=object)

(heatPlot['Crime Against Category']).unique()

array(['PROPERTY', 'SOCIETY', 'PERSON'], dtype=object)

(heatPlot['Offense']).unique()

array(['Theft of Motor Vehicle Parts or Accessories', 'Robbery',
       'Destruction/Damage/Vandalism of Property',
       'Theft From Motor Vehicle', 'Wire Fraud', 'Counterfeiting/Forgery',
       'Drug/Narcotic Violations', 'All Other Larceny',
       'Pornography/Obscene Material', 'Extortion/Blackmail',
       'Shoplifting', 'Motor Vehicle Theft', 'Theft From Building',
       'Driving Under the Influence', 'Stolen Property Offenses',
       'Credit Card/Automated Teller Machine Fraud', 'Identity Theft',
       'False Pretenses/Swindle/Confidence Game', 'Impersonation',
       'Weapon Law Violations', 'Hacking/Computer Invasion',
       'Embezzlement', 'Bad Checks', 'Pocket-picking',
       'Drug Equipment Violations', 'Purchasing Prostitution',
       'Purse-snatching', 'Theft From Coin-Operated Machine or Device',
       'Liquor Law Violations', 'Arson',
       'Curfew/Loitering/Vagrancy Violations', 'Prostitution',
       'Assisting or Promoting Prostitution', 'Drunkenness',
       'Animal Cruelty', 'Welfare Fraud',
       'Operating/Promoting/Assisting Gambling', 'Bribery',
       'Betting/Wagering', 'Gambling Equipment Violation',
       'Trespass of Real Property', 'Aggravated Assault',
       'Simple Assault', 'Burglary/Breaking & Entering', 'Intimidation'],
      dtype=object)

#('LARCENY-THEFT', 220201),('BURGLARY/BREAKING&ENTERING', 62254),('MOTOR VEHICLE THEFT', 45195),('DESTRUCTION/DAMAGE/VANDALISM OF PROPERTY', 42639),('FRAUD OFFENSES', 35439)]
def isPopularOffense(offense):
    if offense == 'LARCENY-THEFT':
        return 'white'
    elif offense == 'BURGLARY/BREAKING & ENTERING':
        return 'red'
    elif offense == 'MOTOR VEHICLE THEFT':
        return 'blue'
    elif offense == 'DESTRUCTION/DAMAGE/VANDALISM OF PROPERTY':
        return 'yellow'
    elif offense == 'FRAUD OFFENSES':
        return 'green'

heatPlotIT=heatPlot[0:3000]
heatPlotIT

count_LARCENY_THEFT=0 
count_BURGLARY_BREAKING_and_ENTERING=0 
count_MOTOR_VEHICLE_THEFT=0 
count_DESTRUCTION_DAMAGE_VANDALISM_OF_PROPERTY=0 
count_FRAUD_OFFENSES=0 


for point in heatPlotIT.index:
    if heatPlotIT.loc[point]['Offense Parent Group']=='LARCENY-THEFT':
        count_LARCENY_THEFT+=1
    elif heatPlotIT.loc[point]['Offense Parent Group']=='BURGLARY/BREAKING&ENTERING':
        count_BURGLARY_BREAKING_and_ENTERING+=1
    elif heatPlotIT.loc[point]['Offense Parent Group']=='MOTOR VEHICLE THEFT':
        count_MOTOR_VEHICLE_THEFT+=1
    elif heatPlotIT.loc[point]['Offense Parent Group']=='DESTRUCTION/DAMAGE/VANDALISM OF PROPERTY':
        count_DESTRUCTION_DAMAGE_VANDALISM_OF_PROPERTY+=1
    elif heatPlotIT.loc[point]['Offense Parent Group']=='FRAUD OFFENSES':
        count_FRAUD_OFFENSES+=1

    map_parentGroup.add_child(folium.CircleMarker(location=locationsCrimeList[point], fill='true',radius = 6,popup= 'Hi', fill_color=isPopularOffense(heatPlotIT.loc[point]['Offense Parent Group']),color = 'clear',fill_opacity=1))


map_parentGroup

def isCrimeAgainst(group):
    if group == 'PROPERTY':
        return 'red'
    elif group == 'SOCIETY':
        return 'green'
    elif group == 'PERSON':
        return 'blue'
    elif group == 'NOT_A_CRIME':
        return 'white'

count_PROPERTY=0 
count_SOCIETY=0 
count_PERSON=0 
count_NotACrime=0 


for point in heatPlotIT.index:
    if heatPlotIT.loc[point]['Crime Against Category']=='PROPERTY':
        count_PROPERTY+=1
    elif heatPlotIT.loc[point]['Crime Against Category']=='SOCIETY':
        count_SOCIETY+=1
    elif heatPlotIT.loc[point]['Crime Against Category']=='PERSON':
        count_PERSON+=1
    elif heatPlotIT.loc[point]['Crime Against Category']=='NOT_A_CRIME':
        count_NotACrime+=1


    map_crimeAgainst.add_child(folium.CircleMarker(location=locationsCrimeList[point], fill='true',radius = 6,popup= 'Hi', fill_color=isCrimeAgainst(heatPlotIT.loc[point]['Crime Against Category']),color = 'clear',fill_opacity=1))


map_crimeAgainst

	Report Number	Offense ID	Offense Start DateTime	Offense End DateTime	Report DateTime	Group A B	Crime Against Category	Offense Parent Group	Offense	Offense Code	Precinct	Sector	Beat	MCPP	100 Block Address	Longitude	Latitude
0	2020-044620	12605873663	2020-02-05 10:10:00	NaN	2020-02-05 11:24:31	A	SOCIETY	DRUG/NARCOTIC OFFENSES	Drug/Narcotic Violations	35A	W	Q	Q1	MAGNOLIA	32XX BLOCK OF 23RD AVE W	-122.385974	47.649387
1	2020-044452	12605598696	2020-02-03 08:00:00	2020-02-04 08:00:00	2020-02-05 10:06:28	A	PROPERTY	LARCENY-THEFT	Theft of Motor Vehicle Parts or Accessories	23G	N	J	J3	ROOSEVELT/RAVENNA	63XX BLOCK OF 5TH AVE NE	-122.323399	47.675118
2	2020-044465	12605567653	2020-02-02 20:30:00	2020-02-02 21:30:00	2020-02-05 09:39:33	A	PROPERTY	ROBBERY	Robbery	120	N	U	U3	ROOSEVELT/RAVENNA	26TH AVE NE / NE BLAKELEY ST	-122.299552	47.666384
3	2020-044225	12605174036	2020-02-05 01:17:00	2020-02-05 02:21:00	2020-02-05 03:30:55	A	PROPERTY	DESTRUCTION/DAMAGE/VANDALISM OF PROPERTY	Destruction/Damage/Vandalism of Property	290	W	Q	Q1	MAGNOLIA	22XX BLOCK OF W RAYE ST	-122.384865	47.642927
4	2020-044121	12605097782	2020-02-04 22:41:00	NaN	2020-02-05 00:59:41	A	PROPERTY	DESTRUCTION/DAMAGE/VANDALISM OF PROPERTY	Destruction/Damage/Vandalism of Property	290	S	R	R1	NORTH BEACON HILL	25XX BLOCK OF 14TH AVE S	-122.314719	47.580248
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
874064	2013-247888	7687554356	2013-07-13 01:00:00	NaN	2013-07-13 06:37:00	A	PROPERTY	MOTOR VEHICLE THEFT	Motor Vehicle Theft	240	SW	F	F1	PIGEON POINT	41XX BLOCK OF 20TH AVE SW	-122.359028	47.565702
874065	2013-227022	7682354808	2013-06-26 11:00:00	2013-06-29 06:30:00	2013-06-29 09:43:00	A	PROPERTY	MOTOR VEHICLE THEFT	Motor Vehicle Theft	240	W	Q	Q3	QUEEN ANNE	1ST AVE N / VALLEY ST	-122.355386	47.626251
874066	2012-045494	7672915592	2012-02-14 15:04:00	NaN	2012-02-14 15:04:00	A	PROPERTY	LARCENY-THEFT	Shoplifting	23C	N	U	U1	ROOSEVELT/RAVENNA	73XX BLOCK OF ROOSEVELT WAY NE	-122.317464	47.681894
874067	2010-328592	7692227482	2010-09-19 16:59:00	NaN	2010-09-19 16:59:00	A	PROPERTY	LARCENY-THEFT	Shoplifting	23C	W	M	M3	DOWNTOWN COMMERCIAL	5XX BLOCK OF PIKE ST	-122.335018	47.610909
874068	2010-064656	7686420892	2010-02-25 18:00:00	2010-02-26 07:30:00	2010-02-26 07:54:00	A	PROPERTY	MOTOR VEHICLE THEFT	Motor Vehicle Theft	240	W	Q	Q2	QUEEN ANNE	26XX BLOCK OF 1ST AVE N	-122.355546	47.643681

	Report Number	Offense ID	Offense Start DateTime	Report DateTime	Group A B	Crime Against Category	Offense Parent Group	Offense	Precinct	Sector	Beat	MCPP	100 Block Address	Longitude	Latitude
1	2020-044452	12605598696	2020-02-03 08:00:00	2020-02-05 10:06:28	A	PROPERTY	LARCENY-THEFT	Theft of Motor Vehicle Parts or Accessories	N	J	J3	ROOSEVELT/RAVENNA	63XX BLOCK OF 5TH AVE NE	-122.323399	47.675118
2	2020-044465	12605567653	2020-02-02 20:30:00	2020-02-05 09:39:33	A	PROPERTY	ROBBERY	Robbery	N	U	U3	ROOSEVELT/RAVENNA	26TH AVE NE / NE BLAKELEY ST	-122.299552	47.666384
3	2020-044225	12605174036	2020-02-05 01:17:00	2020-02-05 03:30:55	A	PROPERTY	DESTRUCTION/DAMAGE/VANDALISM OF PROPERTY	Destruction/Damage/Vandalism of Property	W	Q	Q1	MAGNOLIA	22XX BLOCK OF W RAYE ST	-122.384865	47.642927
7	2020-044104	12605029468	2020-02-04 20:30:00	2020-02-04 23:31:52	A	PROPERTY	DESTRUCTION/DAMAGE/VANDALISM OF PROPERTY	Destruction/Damage/Vandalism of Property	N	J	J3	PHINNEY RIDGE	71XX BLOCK OF GREENWOOD AVE N	-122.355309	47.680527
9	2020-044013	12605015456	2020-02-04 20:20:00	2020-02-04 22:28:09	A	PROPERTY	DESTRUCTION/DAMAGE/VANDALISM OF PROPERTY	Destruction/Damage/Vandalism of Property	N	U	U1	SANDPOINT	RAVENNA AVE NE / NE 95TH ST	-122.304891	47.697564